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    主题：求助，各位大虾帮忙看一下应该用何统计方法？方差不齐怎么办？
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          求助，各位大虾帮忙看一下应该用何统计方法？方差不齐怎么办？
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             求助，各位大虾帮忙看一下应该用何统计方法？方差不齐怎么办？
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               2007年8月12日 上午5:16
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              <p>
               我的实验设计的目的是观察A、B两个因子在某种疾病中随时间的变化规律以及了A和B两者有无相关性,实验设了0h、1h、3h等10个时间点，每个时间点一组，每组5只动物，对照组是0h的。A、B两个因子的测定方法是做的免疫组化，用图形分析软件测得原始数据，我只列了一个指标的数据，数值特别小，另一组数据的值也挺小的。有一部分结果“A原始数据”后我用spss统计了一下，用单因素方差分析，LSD，SNK两两比较，虽然方差不齐，但是LSD，SNK两两比较结果比较理想，另外个体数有多有少。我补充了一些数据存在“A原始数据补充”里面，统计之后方差不齐，两两比较结果也不理想。请问各位大虾应该用什么统计方法，用单因素方差分析是不是必须作方差齐性检验，现在方差不齐怎么办，补充后的数据能不能调整一下得到我理想的结果？
               <br/>
               多谢各位
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               2007年8月12日 下午12:18
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              <p>
               我不会添加附件，把"A原始数据"直接粘贴如下，不好意思
               <br/>
               group    X
               <br/>
               1    0.000888472
               <br/>
               1    0.000689256
               <br/>
               1    0.000415624
               <br/>
               1    0.002264114
               <br/>
               2    0.002786453
               <br/>
               2    0.000888472
               <br/>
               2    0.000888472
               <br/>
               2    0.001009402
               <br/>
               2    3.4579E-05
               <br/>
               3    0.004465076
               <br/>
               3    0.001313867
               <br/>
               3    0.005237868
               <br/>
               3    0.009105735
               <br/>
               3    0.003015855
               <br/>
               3    0.005437352
               <br/>
               3    0.002588719
               <br/>
               3    0.005273232
               <br/>
               4    0.005776052
               <br/>
               4    0.00476333
               <br/>
               4    0.010500214
               <br/>
               4    0.00685363
               <br/>
               4    0.013283157
               <br/>
               4    0.005833868
               <br/>
               4    0.015189565
               <br/>
               4    0.01407485
               <br/>
               4    0.0218133
               <br/>
               4    0.011821985
               <br/>
               4    0.009255084
               <br/>
               4    0.005854743
               <br/>
               4    0.00878654
               <br/>
               4    0.011849724
               <br/>
               4    0.009953075
               <br/>
               4    0.003036451
               <br/>
               4    0.009645864
               <br/>
               4    0.008206013
               <br/>
               4    0.012270887
               <br/>
               4    0.011121147
               <br/>
               5    0.016683601
               <br/>
               5    0.006003097
               <br/>
               5    0.009424236
               <br/>
               5    0.004188834
               <br/>
               5    0.006522528
               <br/>
               5    0.006775719
               <br/>
               5    0.014093961
               <br/>
               5    0.011969405
               <br/>
               5    0.02176504
               <br/>
               5    0.012814761
               <br/>
               5    0.02005474
               <br/>
               5    0.013943003
               <br/>
               6    0.010584103
               <br/>
               6    0.01322352
               <br/>
               6    0.012247728
               <br/>
               6    0.021653265
               <br/>
               6    0.010582834
               <br/>
               6    0.022382406
               <br/>
               6    0.022009868
               <br/>
               6    0.008556528
               <br/>
               6    0.005413855
               <br/>
               6    0.005653603
               <br/>
               6    0.008533086
               <br/>
               6    0.010686343
               <br/>
               7    0.012913247
               <br/>
               7    0.00851636
               <br/>
               7    0.013506202
               <br/>
               7    0.011707528
               <br/>
               7    0.006386773
               <br/>
               7    0.01168742
               <br/>
               7    0.013708478
               <br/>
               7    0.009907122
               <br/>
               7    0.009381331
               <br/>
               7    0.017864862
               <br/>
               7    0.020012762
               <br/>
               8    0.012766285
               <br/>
               8    0.010429821
               <br/>
               8    0.016632306
               <br/>
               8    0.01143951
               <br/>
               8    0.013884772
               <br/>
               8    0.011704233
               <br/>
               8    0.013298895
               <br/>
               8    0.010813833
               <br/>
               8    0.014781282
               <br/>
               8    0.01227261
               <br/>
               8    0.016959599
               <br/>
               8    0.011142259
               <br/>
               8    0.012080392
               <br/>
               8    0.012652719
               <br/>
               8    0.013754495
               <br/>
               8    0.008304909
               <br/>
               8    0.011321079
               <br/>
               8    0.012807605
               <br/>
               8    0.029219874
               <br/>
               8    0.020293643
               <br/>
               8    0.034585616
               <br/>
               9    0.005969257
               <br/>
               9    0.002264114
               <br/>
               9    0.006892562
               <br/>
               9    0.004975659
               <br/>
               9    0.006128678
               <br/>
               9    0.006892562
               <br/>
               9    0.006886433
               <br/>
               9    0.004294847
               <br/>
               9    0.004156242
               <br/>
               10    0.005297307
               <br/>
               10    0.003593044
              </p>
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               2007年8月12日 下午12:22
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              <p>
               补充后的A原始数据
              </p>
              <p>
               N          A                B          C                D
               <br/>
               0.000888472    0.002786453    0.004465076    0.005776052    0.016683601
               <br/>
               0.000689256    0.000888472    0.001313867    0.00476333    0.006003097
               <br/>
               0.000415624    0.000888472    0.005237868    0.010500214    0.009424236
               <br/>
               0.002264114    0.001009402    0.009105735    0.00685363    0.004188834
               <br/>
               0.000366202    3.4579E-05    0.003015855    0.013283157    0.006522528
               <br/>
               1.07154E-05    9.83827E-05    0.005437352    0.005833868    0.006775719
               <br/>
               0.000791803    0.003200358    0.002588719    0.015189565    0.014093961
               <br/>
               0.002177929    0.001049897    0.005273232    0.01407485    0.011969405
               <br/>
               0.002070233    0.002092883    0.004216853    0.0218133    0.02176504
               <br/>
               0.001443137    0.002588235    0.004229009    0.011821985    0.012814761
               <br/>
               0.000741248    0.001768555    0.003487633    0.009255084    0.02005474
               <br/>
               0.000888037    0.001047432    0.004299448    0.005854743    0.013943003
               <br/>
               0.000414024    0.00103997    0.005710449    0.00878654    0.008252633
               <br/>
               0.000586579    0.000520713    0.004938038    0.011849724    0.009766161
               <br/>
               0.00166225    0.001733546    0.0036581    0.009953075    0.006323449
               <br/>
               0.001118076    0.001645638    0.004304794    0.003036451    0.00976243
               <br/>
               0.000626126    0.000250525    0.005270267    0.009645864    0.008688986
               <br/>
               0.000471718    0.000547041    0.004611703    0.008206013    0.007196597
               <br/>
               0.002437512    0.001756023    0.005484778    0.012270887    0.009792417
               <br/>
               0.011121147
              </p>
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               2007年8月12日 下午12:23
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              <p>
               补充后的A原始数据
              </p>
              <p>
               E    F    G    H    I
               <br/>
               0.010584103    0.012913247    0.012766285    0.005969257    0.005297307
               <br/>
               0.01322352    0.00851636    0.010429821    0.002264114    0.003593044
               <br/>
               0.012247728    0.013506202    0.016632306    0.006892562    0.004495415
               <br/>
               0.021653265    0.011707528    0.01143951    0.004975659    0.006710136
               <br/>
               0.010582834    0.006386773    0.013884772    0.006128678    0.004341115
               <br/>
               0.022382406    0.01168742    0.011704233    0.006892562    0.004821567
               <br/>
               0.022009868    0.013708478    0.013298895    0.006886433    0.003601093
               <br/>
               0.008556528    0.009907122    0.010813833    0.004294847    0.002854674
               <br/>
               0.005413855    0.009381331    0.014781282    0.004156242    0.004836166
               <br/>
               0.005653603    0.017864862    0.01227261    0.005495593    0.00519431
               <br/>
               0.008533086    0.020012762    0.016959599    0.004835402    0.005085673
               <br/>
               0.010686343    0.014053603    0.011142259    0.005133641    0.003835759
               <br/>
               0.011161856    0.010830204    0.012080392    0.003625913    0.002799929
               <br/>
               0.011505427    0.016434535    0.012652719    0.005773221    0.003988836
               <br/>
               0.015945678    0.013528448    0.013754495    0.008027797    0.005414565
               <br/>
               0.011877901    0.011732925    0.008304909    0.004790774    0.004318926
               <br/>
               0.008467099    0.015059235    0.011321079    0.004504443    0.005009932
               <br/>
               0.011492426    0.011236063    0.012807605    0.005443178    0.006210313
               <br/>
               0.011398502    0.016107284    0.029219874    0.006213161    0.002668063
               <br/>
               0.020293643    0.005590503    0.004490307
               <br/>
               0.034585616
              </p>
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               2007年8月13日 下午2:14
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              <p>
               没大看明白数据，似乎每个group的个数不一样多啊
               <br/>
               但是至少据我所知，方差分析要求数据1,正态2齐方差3相互独立。如果不满足的话，可以考虑非参数检验。
               <br/>
               而且看这个数据似乎是重复测量设计资料，即使齐方差处理起来也挺复杂的。
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               2007年8月13日 下午3:36
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              <p>
               那些数据我测出来一部分试着统计的，所以每组个体数不一样，因为这些数的统计结果LSD和SNK在假设方差齐的情况下结果很理想，我主要想说明我理想的两两比较结果，就发上去了，后来补充数据后再统计方差也不齐，两两比较结果也不理想，调整数据也没个方向，只好求助     ，拜托各位高手了
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               2007年8月13日 下午3:41
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              <p>
               我考虑过用重复测量方差分析，因为我的实验动物到相应的时间点就处死了，只测一次结果，不像重复测量方差分析中一个个体在不同的时间点多次测量，所以就放弃了这种方法，选择了单因素方差分析，不知道我的想法对不对？
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               2007年8月13日 下午7:25
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              <p>
               感觉这是一个很有意思的试验。不过，你对试验的描述太模糊，结合你的头２贴，我都看不懂你的意思。最好你再说清楚一点，group表示什么（是指你第一贴中的什么），X是什么，为什么group中的１有４个观测值，而２有５个，３有８个，等等。方差非齐性的问题可以解决，但依据你提供的试验背景材料。只要你能提供我要的条件，我可以帮你解决这个问题。
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               2007年8月14日 上午2:25
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              <p>
               如果是A、B两组观察，每组10个时间点，每时间点每组观察5只，观察后处死的话，那么就是说总共用了100只动物。
               <br/>
               如果方差不齐解决的话，做一个2*10的析因方差分析好不好呢？
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               2007年8月14日 上午3:30
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              <p>
               真不好意思，我的实验描述能力太差了，浪费了大家的时间，谢谢楼上的提醒，要不我还以为说明白了呢。group表示实验分组1表示0h时间点（对照组N）的，2表示1h时间点（实验组A），3表示3h时间点的（实验组B）依此类推。x表示给观测值，每组的观测值不均衡是因为我作实验有了一部分数据（A原始数据）后就试着统计了一下，虽然方差不齐，但两两比较的结果比较理想。后来补充数据（补充后的A原始数据）后不仅方差不齐，两两比较的结果也不理想，为了说明我的理想的两两比较结果，我A原始数据发了上去，可能误导了大家，抱歉！
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               2007年8月14日 上午3:38
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               <p>
                [b]引用第8楼[i]TTG[/i]于[i]2007-08-14 09:25[/i]发表的“”[/b]:
                <br/>
                如果是A、B两组观察，每组10个时间点，每时间点每组观察5只，观察后处死的话，那么就是说总共用了100只动物。
                <br/>
                如果方差不齐解决的话，做一个2*10的析因方差分析好不好呢？
               </p>
              </blockquote>
              <p>
               我还是没说太明白 ，我的实验是按时间点分组，10个时间点10组，每组5只动物，每只动物处死后测量A 、B两种指标，共用了50只动物。
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               2007年8月14日 上午4:21
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              <p>
               首先，不是重复测量，其次，如果看上去你的10个时间点就是10个处理组的话，，有点，，不知道处理因素和时间是什么关系？如果没关系，那就没那么复杂，除非你A,B两个指标放在一起作为一个整体做多元方差分析，如果A,B指标分开做的话，就是一个单因素方差分析，10个组，每个组设计的时候是5例，由于动物不听话，东一只，西一只的死，搞得每组样本例数多多少少的，方差不齐的样子，，，我高度疑惑，你的时间点和group到底是不是一回事？是的话，，，有点搞不懂，处理因素和时间的作用怎么分开呢？
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               2007年8月14日 上午5:04
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               我还是不大明白。看你补充后的A的原始数据，N、A、B这些应该是对应不同的时间点吧，正好是10个时间点，但每组是5只动物，应该只有5个观测值啊，为什么每组的观测值反而是19到20个不等呢？
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               2007年8月14日 上午9:42
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               14 楼
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               实验设计是按每组5只设计的，动物模型是手术建立脑缺血的模型，统一建立模型后按设计的时间点取材，0h、1h、3h、6h、9h、12h、24h、48h、72h、120h等10个时间点，0h是对照组也就是假手术组，分别用N、A、B、C、D、E、F、G、H、I表示。对实验动物手术后等时间点的过程中不对动物作其他处理。像楼上robustreg说的“由于动物不听话，东一只，西一只的死，搞得每组样本例数多多少少的”后来又补充了几只动物，补充数据。
               <br/>
               实验动物取材后，标本切片，每个动物两张切片分别用a、b两个因子抗体做免疫组化染色，每张切片显微镜下拍4张照片，用图形分析软件测得到a、b两个因子的原始数据每张照片得一个数，5只动物得20个数，代表这一组的个体数。
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               2007年8月14日 上午10:01
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               我也有点晕了
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